• Kajal Thorave
• Mrs.S.A.Hadke
• Samruddhi Gadadare
• Shital Shete
• Supriya Shinde

Inside the field of online education, the main part is an online test. From time to time it becomes difficult to evaluate the scoring of these tests, quizzes or difficult answers. Also, it becomes hard for a teacher to observe all these answers for huge class. The proposed system should be able to forecast scores for descriptive answers and approach makes use of data mining techniques of the k-nearest neighbors (KNN) algorithm to forecast the score. This paper presents text classification for subjective scoring using k-nearest neighbors in order to automatically score the subjective answer.The product can be used for evaluating scores of expressive answers by using TF-IDF and machine learning classifier. The proposed similarity is based on word matching and word ordering. If the pair of text match words/phrases and have the same order, the match is high. The results suggest the new approach is able to forecast the subjective score and achieve the highest analyticalcorrectness than the standard classifiers. To decrease human efforts to examine descriptive answers, we are developing a system that will automatically estimate the answers and forecast score using TF-IDF and KNN. As a result, the proposed method can be a useful tool for instructors. This product can provide correct score than manual analysis by teachers. It can be a useful tool that helps the instructor to score the subjective answer.

"Text Classification for Subjective Scoring Using K-Nearest Neighbors", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.659-663, May-2019, Available :http://www.jetir.org/papers/JETIR1905C94.pdf

"Text Classification for Subjective Scoring Using K-Nearest Neighbors", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp659-663, May-2019, Available at : http://www.jetir.org/papers/JETIR1905C94.pdf